          numd (count (:classes norm-dataset)) ; number of data
          groupn (quot numd k)]                ; number of one group's element
      (->> (take k (iterate #(+ % groupn) 0))
-          (pmap #(let [[eva exp-data] (_cross-validate norm-dataset % (+ % (dec groupn)) L)]
-                   (dorun (println exp-data))
-                   eva))
-          (reduce +)          
+          (pmap #(let [eva (_cross-validate norm-dataset % (+ % (dec groupn)) L)]
+                   (dorun (println eva))
+                   (:Accuracy eva)))
+          (reduce +)
           (* (/ 1 k))))))

    (and (= pred -1) (= fact -1)) (assoc exp :TN (inc (:TN exp)))
    (and (= pred -1) (= fact 1)) (assoc exp :FN (inc (:FN exp)))))

(defn evaluation
  ([results expdata facts]
   {:pre [(coll? results) (coll? facts) (map? expdata)]}
   (let [numd (count results)]
     (->> (pmap #(= %1 %2) results facts)
          (filter true?)
          (count)
          (#(/ % numd))))))

(defn select-count
  ([cond coll]

         betas (c/to-vect (c/mmult (pseudo-inverse-matrix H) T))]
     (Model. ass bs betas))))

(defn predict
  ([model xs]
   {:pre [(instance? Model model) (coll? xs)]}
   (predict (:ass model) (:bs model) (:betas model) xs))
